Method and System for Learning Call Analysis

ABSTRACT

A system and method are presented for learning call analysis. Audio fingerprinting may be employed to identify audio recordings that answer communications. In one embodiment, the system may generate a fingerprint of a candidate audio stream and compare it against known fingerprints within a database. The system may also search for a speech-like signal to determine if the end point contains a known audio recording. If a known audio recording is not encountered, a fingerprint may be computed for the contact and the communication routed to a human for handling. An indication may be made as to if the call is indeed an audio recording. The associated information may be saved and used for future identification purposes.

BACKGROUND

The present invention generally relates to telecommunication systems and methods. More particularly, the present invention pertains to the detection of recorded audio by automated dialer systems in contact centers.

SUMMARY

A system and method are presented for learning call analysis. Audio fingerprinting may be employed to identify audio recordings that answer communications. In one embodiment, the system may generate a fingerprint of a candidate audio stream and compare it against known fingerprints within a database. The system may also search for a speech-like signal to determine if the end point contains a known audio recording. If a known audio recording is not encountered, a fingerprint may be computed for the contact and the communication routed to a human for handling. An indication may be made as to if the call is indeed an audio recording. The associated information may be saved and used for future identification purposes.

In one embodiment, a system for learning interactions analysis is described, comprising: means for communication; means for managing interactions through said means for communication; means for analyzing the progress of said interactions; and means for storing information.

In one embodiment, a system for learning call analysis is provided, comprising: a telephony service module; a media server; a database; an automated dialer; a network; and one or more workstations.

In another embodiment, a method for call learning in a communication system is provided, comprising the steps of: selecting a number of contacts from a database to contact; performing a database lookup for existing fingerprints associated with a contact; initiating a communication with a contact; determining whether a fingerprint match exists; and computing a new fingerprint for the contact if a match does not exist.

In another embodiment, a method for call learning in a communication system is provided, comprising the steps of: selecting a number of contacts from the database that will be contacted; performing a lookup for existing fingerprints; initiating a communication with a contact; determining whether speech has been detected; and computing a new fingerprint for the contact if an existing fingerprint is not found.

In another embodiment, a method for routing communications in a communication system is provided, comprising the steps of: initiating a communication with a contact; and determining whether said contact, is associated with a known target wherein: if said contact is not associated with a known target, routing said communication to a human, and performing an action, and if said contact is associated with a known target, performing an other action.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating the basic components of an embodiment of a learning call analysis system.

FIG. 2 is a flowchart illustrating an embodiment of the process of call learning.

FIG. 3 is a table illustrating an embodiment of an automated dialer record.

FIG. 4 is a table illustrating an embodiment of an automated dialer record.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended. Any alterations and further modifications in the described embodiments, and any further applications of the principles of the invention as described herein are contemplated as would normally occur to one skilled in the art to which the invention relates.

In an embodiment of a contact center scenario, outbound communications, such as phone calls may be made automatically by a class of devices known as “automated dialers” or “autodialers”. In another embodiment of a contact center scenario, outbound communications may also be placed manually. A number of humans, or agents, may be available to join into communications that are determined to reach a live person. When a call is initiated, a determination may be made as to whether the call was answered by a live speaker. A contact center may become more efficient by not having agents involved in a communication until it is determined that there is a live person at the called end with whom the agent may speak.

Answering machine detection (AMD) is critical to contact centers utilizing automated dialer systems because most calls placed often result in pre-recorded audio from a machine or other automated system. Every audio recording or other automated system that is incorrectly detected as a live speaker may be routed to an agent for handling. As a result, agents may begin to assume an audio recording is at the other end of the call and mistakenly hang up on a live person, sound surprised, lose their train of thought, etc. AMD employs various signal processing algorithms to classify the entity that picks up a communication into categories, for example, such as answering machines, or recorded audio, and live speakers. The accuracy of these algorithms may depend on various parameters and requires trading off AMD rate and live speaker detection (LSD) rate. For example, biasing an autodialer towards a high AMD rate may result in more live speakers being classified incorrectly as recorded audio and hung-up on by the autodialer and vice-versa.

Some countries as well as applications, such as high-value dialing for example, do not allow or utilize AMD because of false positives. An example of a false positive may include a live speaker who is classified as an answering machine. As a result, AMD may be disabled or tuned heavily toward LSD. A large number of audio recordings may thus be routed to agents.

In another example, an autodialer operation may contact the same phone number multiple times in a day to try and reach a live speaker. If the called number connects to an audio recording that call analysis cannot correctly detect. For example, the audio recording “Hi <long pause> We aren't available right now . . . ” may result in the system not detecting that a human is not speaking. As a result, each time that number is dialed it may be mistakenly routed to an agent. By learning the fingerprint of a specific audio recording, the autodialer may prevent an audio recording from being repeatedly routed to an agent. If an audio recording associated with a contact is altered, however, the system may have to relearn the fingerprint of the audio recording the next time that number is dialed. This information may be added to the record of the contact and stored for future use.

A fingerprint, in the field of Acoustic Fingerprinting, may be a passive unique trait to identify a particular thing. A system may generate a fingerprint of a candidate audio stream and compare the newly generated fingerprint against a database of known fingerprints. The fingerprints may be used in communications systems for routing purposes. Humans may want to increase the opportunity to interact with another human instead of with an audio recording. Thus, contacts having fingerprints identifying audio recordings may not be handled by a human and routed otherwise, for example.

By learning about audio recordings that are missed, learning call analysis, in one embodiment, allows contact centers using automated dialers to turn down their AMD bias and turn up their LSD bias. As a result, contact centers may be able to maximize the number of live speakers that are routed to agents. The contact center may become aware that the increased number of audio recordings that initially come through to agents. The audio recordings may be marked as a recording and may subsequently not be re-routed to agents when they are recognized.

FIG. 1 is a diagram illustrating the basic components in an embodiment of a learning call analysis system, indicated generally at 100. The basic components of a system 100 may include: a telephony services module 105, which may include a media server 115; an automated dialer 110; a network 120; an agent workstation 125, which may include a work station computer 128 coupled to a display 127, and a telephone 126; a database 130; and a call endpoint 135.

The telephony services module 105 may include a media server 115. In one embodiment, the telephony services module 105 may comprise an application programming interface (API) that receives audio recording fingerprints through the automated dialer 110 and sends the fingerprints to the media server 115 when placing a call. The media server 115 may receive answering audio recording fingerprints and use them as part of the call analysis. The media server 115 may also be able to generate fingerprints and send these to the telephony services module 105 when requested.

In one embodiment, the automated dialer 110 may comprise a device that automatically dials telephone numbers. In another embodiment, the automated dialer may comprise software. An example may be Interactive Intelligence, Inc.'s, Interaction Dialer®. In one embodiment, the automated dialer 110 may have a lookup or caching mechanism that matches phone numbers, or other contact information, to existing audio recording fingerprints for communications about to be placed. In one embodiment, when a call is sent to an agent and identified as an audio recording, the automated dialer 110 may request the fingerprints for that call from the telephony services 105 and media server 115 and update database tables.

The network 120 may be in the form of a VoIP, a network/internet based voice communication, PTSN, mobile phone network, Local Area Network (LAN), Municipal Area Network (MAN), Wide Area Network (WAN), such as the Internet, a combination of these, or such other network arrangement as would occur to those skilled in the art. The operating logic of system 100 may be embodied in signals transmitted over network 120, in programming instructions, dedicated hardware, or a combination of these. It should be understood that any number of computers 128 can be coupled together by network 120.

The agent workstation 125 may include a work station computer 128 coupled to a display 127. Workstation computers 128 may be of the same type, or a heterogeneous combination of different computing devices. Likewise, displays 127 may be of the same type or a heterogeneous combination of different visual devices. It should be understood that while one work station 125 is described in the illustrative embodiment, more may be utilized. Contact center applications of system 100 typically include many more workstations of this type at one or more physical locations, but only one is illustrated in FIG. 1 to preserve clarity. In another embodiment, agents may not even be utilized, such as in a system that regularly leaves messages, but provides an IVR with a message and options if a live speaker is encountered. Further it should be understood that while a contact center is mentioned and agents are referred to, it is within the scope of this material not to limit application to a contact center setting.

A digital telephone 126 may be associated with agent workstation 125. Additionally, a digital telephone 126 may be integrated into the agent computer 128 and/or implemented in software. It should be understood that a digital telephone 126, which is capable of being directly connected to network 100, may be in the form of handset, headset, or other arrangement as would occur to those skilled in the art. It shall be further understood that the connection from the network 120 to an agent workstation 125 can be made first to the associated workstation telephone, then from the workstation telephone to the workstation computer by way of a pass through connection on the workstation telephone. Alternatively, two connections from the network can be made, one to the workstation telephone and one to the workstation computer. Although not shown to preserve clarity, an agent workstation 125 may also include one or more operator input devices such as a keyboard, mouse, track ball, light pen, tablet, mobile phone and/or microtelecommunicator, to name just a few representative examples. Additionally, besides display 127, one or more other output devices may be included such as loudspeaker(s) and/or a printer.

The database 130 may house the automated dialer 110 records. The records contained in the database 130 may enable the system 100 to determine whether a fingerprint is present The records may also enable the system to determine whether an audio recording is present at the other end of a communication.

In one embodiment, the call endpoint 135 may represent the endpoint of a call placed by the system through the network 120 and there is an answer. The answer may be by any entity, such as a live speaker or an audio recording, for example.

As illustrated in FIG. 2, a process 200 for illustrating call learning is provided. The process 200 may be operative in any or all of the components of the system 100 (FIG. 1).

In step 205, a contact is selected for communication. For example, telephone numbers and matches are cached. In one embodiment, when telephone numbers that are to be dialed are cached, the automated dialer may also cache any fingerprint matches that are found within the system. In one embodiment, multiple fingerprints per contact may be stored. Instances of multiple fingerprints may occur when audio recordings play different announcements based on the time of day or the day of the week, for example. Control is passed to operation 210 and the process 200 continues.

In operation 210, a fingerprint look up is performed for the selected contacts. For example, there may be a fingerprint for any given telephone number of a contact, such as the fingerprint of the audio recording that an agent experienced when a call was placed to that number. In some instances, a telephone number may result in more than one audio recording such as with call forwarding. In at least one embodiment, fingerprints may be found using the telephone number as a reference or via other forms of identification such as a name, customer ID, etc. Control is passed to operation 215 and the process 200 continues.

In operation 215, a call is placed. For example, the call may be performed via the telephony services. In one embodiment, the automated dialer may supply the fingerprint in the API call when the call is initiated. The telephony services may then relay any fingerprints associated with the telephone number to the media server that were identified in the fingerprint lookup. In at least one embodiment, fingerprints may include those of voice mail systems, answering machines, network messages, etc., or any other type of answering service or audio recording. Control is passed to operation 220 and the process 200 continues.

In operation 220, the media server listens for speech or a fingerprint match. In one embodiment, the fingerprints may indicate that the system has encountered the same audio recording previously. The fingerprint may also indicate that the message has changed in the recording. Control is passed to step 225 and process 200 continues.

In operation 225, it is determined whether there is a fingerprint match. If it is determined that there is a fingerprint match, then control is passed to operation 230 and process 200 continues. If it is determined that there is not a fingerprint match, then control is passed to operation 235 and process 200 continues.

The determination in operation 225 may be made based on any suitable criteria. For example, when the media server encounters a recording that has a familiar fingerprint, i.e., it has been learned, then the media server may inform telephony services which in turn may inform the automated dialer that there is a match. If a match for the fingerprint is found, this may indicate an audio recording. However, if there is no match, then the fingerprint could indicate a live person or a changed recording and this will have to be determined by other means, such as the agent. In one embodiment, the agent may indicate in the record the entity at the other end of the call.

In operation 230, a record may be inserted for that telephone number. For example, a record may be inserted into FIG. 4 indicating that the type is “d”, indicating that an audio recording has been detected and the process ends. In one embodiment, other actions may be performed such as disconnecting the communication, scheduling another communication to occur at a later point in time, leaving a message on an answering machine, determining an alternate contact to try, and routing the communication to a handler.

In operation 235, a fingerprint is computed for the telephone number. For example, a unique identifier may be created for the contact. Control is passed to step 240 and process 200 continues.

In operation 240, the communication is routed. For example, a call may be routed to an agent within a contact center. Control is passed to step 245 and process 200 continues.

In operation 245, it is determined whether the end point of the contact has been associated with an audio recording. If it is determined that the end point of the communication is an audio recording, then control is passed to operation 250 and process 200 continues. If it is determined that the end point of the call is not an audio recording, then control is passed to operation 255 and process 200 continues.

The determination in operation 245 may be made based on any suitable criteria. For example, records within the database may be examined. In one embodiment, if a live speaker call is dispositioned by an agent with a wrap-up code indicating that an audio recording has been routed to the agent, then the autodialer requests the audio recording fingerprint from telephony services/media server and writes it to the database. The autodialer may then take the telephone number and the corresponding fingerprint and look it up as described in FIG. 3 below. If the combination of the contact number and fingerprint is found, then the information illustrated in FIG. 4 below may be supplemented. In one embodiment, the indicator “Fingerprint missed” may be input in the record because call analysis should have detected this call as an audio recording, but failed to. If the combination is not found, then the autodialer looks up the contact number. If that record is found, then the autodialer may overwrite the fingerprint for that contact record. Alternatively, multiple fingerprints may be kept according to rules such as the maximum age, maximum number, etc. The autodialer may also insert a type into a record (FIG. 4) indicating that the fingerprint has changed even though the record has been found. For example, in one embodiment, an audio recording may have changed, such as a person changing the message on their answering machine. In another embodiment, the fingerprint of the communication may be added to the database even if a live caller is detected. Storing such information may serve to ensure the agents are not skewing their statistics by pretending to talk to a person and letting an answering machine record the conversation.

In operation 250, a record is inserted associating a contact with a fingerprint and the process ends. In at least one embodiment, multiple fingerprints may be associated with a number. Multiple fingerprints may result in instances where, for example, different audio recording may be played based on the time of day or the day of the week.

In operation 255, a record is inserted. For example, if a phone number is not found, the autodialer may insert a new record into the existing record, of which an embodiment is illustrated in FIG. 3, which includes the ID of the agent that dispositioned the call as an audio recording. The record illustrated in FIG. 4 may also contain information inserted indicating that the communication was a type of “initial detect”, which may indicate that an audio recording is being encountered for the first time and a fingerprint is being added. Control is passed to operation 260 and the process 200 continues.

In operation 260, an agent interacts with the contact, which may be a live person, and the process ends.

FIG. 3 illustrates an embodiment of an autodialer record table, indicated generally at 302. The autodialer record table 302 may be composed of a number of autodialer records 300. While only record 300 a has been illustrated in FIG. 3, any number of records 300 may be provided. An autodialer record 300 may be associated with or resident in the database 130 (FIG. 1). An autodialer record 300 may include an ID field 305, a Contact Identifier field 310, a Fingerprint field 315, and an Identified By field 320.

The ID field 305 may contain a record ID, which is necessarily unique and preferably used consistently. In at least one embodiment, this field may be a primary key. Using the example shown in FIG. 3, record 300 a has an ID of 1.

The Contact Identifier field 310 may contain the telephone number of the contact. This number may have a specified format, such as all digits. Using the example shown in FIG. 3, Record 300 a contains a value of “3175555555” for the telephone number field 310.

The Fingerprint field 315 may contain the fingerprint converted into some form convenient for storage in the database record as would occur to those skilled in the art. It may be fixed or of a variable length or format or comprise a reference to external storage, for example. Although particular examples of fingerprints are presented herein, any sort of unique identifier may be used without departing from the scope of the embodiments herein. Using the example shown in FIG. 3, record 300 a contains a fingerprint field 315 value of “RmluZ2VycHJpbnQx”.

The Identified By field 320 may contain information relating to the means by which the communication was addressed. For example, the field may contain information about how the call was answered. In one embodiment, this information in the record could include the user ID of the agent that identified this fingerprint as an audio recording or it may indicate that the system identified the fingerprint as an audio recording. Using the example shown in FIG. 3, record 300 a contains an identified by field 320 value of “system”, which may indicate that the communication was identified by the system as an audio recording.

FIG. 4 is a table illustrating an embodiment of an autodialer record table, indicated generally at 402. The autodialer record table 402 may be composed of a number of autodialer records 400. While only record 400 a has been illustrated in FIG. 4, any number of records 400 may be provided. An autodialer record 400 may be associated with or resident in the database 130 (FIG. 1). An autodialer record 400 may include an ID field 405, an Insert Date Field 410, and a Type field 415.

The ID field 405 may contain a record ID, which is necessarily unique and preferably used consistently. A record ID may indicate an identifier that is used relevant to each contact account. Although particular examples of IDs are presented herein, any sort of unique identifier may be used without department from the scope of the embodiments herein. In at least one embodiment, this field may be a primary key. Using the example shown in FIG. 3, record 400 a has an ID of 1.

The Insert Date Field 410 may contain the date of the record. In at least one embodiment, this information may have a specified format, such as Aug. 21, 2012. Using the example shown in FIG. 4, record 400 a contains a value of “Aug. 21, 2012” for the insert date field 410.

The type filed 415 may contain information about the call from the system. In at least one embodiment, the call may be expressed in values. For example, “i” for an “initial detect”, “d” for “detect”, “c” for “detect/fingerprint changed”, or “m” for “fingerprint missed”. An “initial detect” may describe an audio recording that is being encountered for the first time. “Detect” may describe that an audio recording has been detected and the associated fingerprint. “Detect/fingerprint changed” may indicate that an audio recording has been detected but the fingerprint has changed. An example may include a newly recorded message on an answering machine. “Fingerprint missed” may indicate a combination of a phone number and a fingerprint matching an existing entry in the table yet the media server did not detect the recording as such. Although particular examples are presented herein, any sort of unique identifier may be used without departing from the scope of the embodiments herein. Using the example shown in FIG. 4, record 400 a contains a value of “c”, which may indicate that an audio recording has been detected but the fingerprint has changed.

Periodically, a contact center may run a script on the record tables in FIGS. 3 and 4 in the database to remove old contacts that have not been called in a specified period of time (e.g., 2 years). Reports may also be generated that can identify such information as to how many audio recordings were not routed to an agent on a given day. Administrators may also periodically run reports on agents with high numbers of associated fingerprints to see if they are mis-characterizing live speakers as audio recordings or vice versa.

In at least one embodiment, to avoid routing answering machines or recorded audio incorrectly classified as a live caller for contacts where the system does not yet have a fingerprint record of the audio recording stored in its database, the algorithmic classification into live caller and audio recording would not be used. Instead, a call to the new telephone number would be placed. The call analysis system may search for a speech-like signal and for fingerprint matches. If there is no matching fingerprint, a fingerprint may be created of the signal and the call disconnected. A message may optionally be played before disconnecting or some other means of handling the call may be employed. The number may be called again at some point in time and if the fingerprint matches, then it can be determined whether the end point is an audio recording. If the fingerprint does not match, then the end point may be a live caller or an audio recording. If the contact attempt results in a live caller, the fingerprint may not be stored in the database. Any subsequent contact attempt to that number would want to utilize the same algorithm again (i.e. call first, take the fingerprint, and call back) as it may be that only confirmed audio recordings, such as where there was a positive fingerprint match in the subsequent call for example, would be stored in the database. For numbers where the system does not yet have a fingerprint of the audio recording, the system may employ statistical or heuristic models to determine the time to place the call where the likelihood of reaching a machine is highest such as when the likelihood of reaching a live caller is lowest. This is in contrast to contacts where fingerprints are present and the system would place communications to maximize the likelihood of reaching a live caller.

In one embodiment, if a fingerprint match is made between a communication endpoint and an existing record in the database, one or more alternate contacts may be used for that record in an attempt to reach a desired endpoint, such as a human. For example, the “Find Me/Follow Me” feature could be employed where several telephone numbers are on record for the same contact. When attempting to locate the contact, the system places calls to these numbers in some order until it reaches the human. After having learned the fingerprints the first time a particular recording is encountered, the system may subsequently be able to identify numbers where an automated system, such as an answering machine or voice mail system, answers without the risk of false-positives of non-fingerprint based AMD.

In one embodiment, where contact centers want to ensure a live caller is not incorrectly classified as an audio recording, calls to numbers for which there is no fingerprint in the database may be performed with AMD disabled. Instead, the call analysis may look for the first utterance represented as a speech-like signal. A fingerprint may be created of the first utterance and returned to the telephony services. The call may then be routed to an agent. When the agent indicates the call end point is an audio recording, the fingerprint may be added to the database for that number. When a number is subsequently called for which an audio recording fingerprint exists, this is passed to the media server. If speech is encountered that matches any of the fingerprints, then it can be determined this may be an audio recording, otherwise, everything else is assumed to be a live caller and the call is passed to agents.

While the invention has been illustrated and described in detail in the drawings and foregoing description, the same is to be considered as illustrative and not restrictive in character, it being understood that only the preferred embodiment has been shown and described and that all equivalents, changes, and modifications that come within the spirit of the inventions as described herein and/or by the following claims are desired to be protected.

Hence, the proper scope of the present invention should be determined only by the broadest interpretation of the appended claims so as to encompass all such modifications as well as all relationships equivalent to those illustrated in the drawings and described in the specification.

Although two very narrow claims are presented herein, it should be recognized the scope of this invention is much broader than presented by the claims. It is intended that broader claims will be submitted in an application that claims the benefit of priority from this application. 

1. A system for learning interactions analysis comprising: a. means for communication; b. means for managing interactions through said means for communication; c. means for analyzing the progress of said interactions; and d. means for storing information.
 2. The system of claim 1, wherein said means for managing interactions comprises an automated dialer.
 3. The system of claim 1, wherein said means for analyzing the progress comprises a media server.
 4. The system of claim 1, wherein said means for analyzing the progress comprises a mechanism for matching segments of an interaction to a plurality of fingerprints.
 5. The system of claim 4, wherein said fingerprints comprise a deterministically condensed digital summary of information.
 6. The system of claim 5, wherein said information comprises an audio signal.
 7. The system of claim 1, wherein said means for analyzing the progress comprises a mechanism to generate fingerprints of segments of said interactions.
 8. The system of claim 7, wherein said segments are dependent on a state of an interaction.
 9. The system of claim 8, wherein said segments comprise a representation of parts of said interactions identified as human speech.
 10. The system of claim 7, wherein said generated fingerprints are capable of being provided to said means for managing interactions.
 11. The system of claim 10, wherein said means for managing interactions comprises a mechanism to correlate said generated fingerprints with an identifier.
 12. The system of claim 11, wherein said identifier comprises the destination of said interaction.
 13. The identifier of claim 11, wherein said identifier comprises a telephone number.
 14. The system of claim 11 wherein said mechanism is capable of associating an identifier of an interaction with a plurality of said fingerprints.
 15. The identifier of claim 14, wherein the identifier comprises a target of interactions.
 16. The system of claim 11 wherein said correlations are capable of being stored in said means for storing information.
 17. The system of claim 16, wherein said means for storing information comprises a database.
 18. The system of claim 16, wherein said means for storing information is capable of appending additional fingerprints to an existing record with the same identifier.
 19. The system of claim 18, wherein said system is capable of identifying and purging fingerprints that have not matched a segment of an interaction for a predetermined period of time.
 20. The system of claim 16, wherein said system is capable of storing said correlations based on a classification of said interaction.
 21. The system of claim 20, wherein said system is capable of classification that identifies the destination of said interaction as one of: automated call answering system and human.
 22. The system of claim 21, wherein said system further comprises a call center agent that is capable of performing said identification.
 23. The system of claim 4 where said plurality of fingerprints comprises one or more of: a. fingerprints active for all interactions; b. fingerprints active for a qualified subset of interactions; and c. fingerprints active for an interaction.
 24. The system of claim 23 where said qualified subset comprises interactions as determined by classifying a destination identifier.
 25. The classifying of claim 24, wherein said classifying comprises a country code of a telephone number.
 26. The system of claim 23 where said fingerprints active for an interaction comprise fingerprints correlated with a destination identifier and stored in said means for storing information.
 27. The system of claim 26, wherein said destination identifier comprises a telephone number.
 28. The system of claim 26 where said fingerprints correlated with a destination identifier have previously been classified as messages of an automated communication answering system.
 29. A system for learning call analysis comprising: a. a telephony service module; b. a media server; c. a database; d. an automated dialer; e. a network; and f. one or more workstations.
 30. The system of claim 29, wherein said telephony service module comprises an application programming interface that receives audio recording fingerprints.
 31. The system of claim 29, wherein said media server is capable of generating fingerprints and providing said generated fingerprints to the telephony service module.
 32. The system of claim 29, wherein said automated dialer comprises a mechanism that matches telephone numbers to existing audio recording fingerprints.
 33. The system of claim 29, wherein a workstation comprises: a. a computer coupled to a display; b. a digital telephone integrated into the computer and capable of being directly connected to the network; and c. at least one operator input device.
 34. The system of claim 29, wherein said database is capable of housing automated dialer records that enable the system to determine at least one of: whether a fingerprint is present and whether an audio recording is present at the other end of a call.
 35. A method for call learning in a communication system, comprising the steps of: a. selecting a number of contacts from a database to contact; b. performing a database lookup for existing fingerprints associated with a contact; c. initiating a communication with a contact; d. determining whether a fingerprint match exists; and e. computing a new fingerprint for the contact if a match does not exist.
 36. The method of claim 35, further comprising the steps of: a. routing the communication to a human; b. determining whether an end point of the communication has been associated with a means for answering a communication; c. creating a record associating the contact with fingerprints including said new fingerprint; and d. saving said record.
 37. The endpoint of claim 36, wherein said endpoint comprises a human.
 38. The method of claim 35, further comprising the step of: a. performing at least one of the following actions if it is determined that a fingerprint match exists: disconnecting the communication, scheduling another communication at a new time, leaving a message with a means for answering a communication, routing the communication to a human, and determine an alternate contact and repeat process.
 39. The method of claim 35, wherein step (c) further comprises the step of supplying existing fingerprints associated with a contact to a media server.
 40. The method of claim 35, wherein a fingerprint comprises a unique identifier.
 41. The method of claim 35, wherein a fingerprint match in step (d) indicates an audio recording.
 42. The method of claim 35, wherein no fingerprint match in step (d) indicates a live person.
 43. The method of claim 35, wherein no fingerprint match in step (d) indicates a changed audio recording.
 44. The method of claim 35, further comprising the steps of: a. performing a record lookup within a database; and b. supplementing a record for a contact with an end result of a call detection.
 45. The method of claim 44, wherein the end result comprises one or more of: detected, changed, and missed.
 46. The method of claim 35, further comprising the steps of: a. disconnecting said communication; b. re-initiating an other communication with said contact; c. determining whether said new fingerprint matches the re-initiated communication; and d. making an association with the means for answering, wherein i. if said new fingerprint matches, then classifying said means as an audio recording, and, ii. if said new fingerprint does not match, then classifying said means as a human.
 47. A method for call learning in a communication system, comprising the steps of: a. selecting a number of contacts from the database that will be contacted; b. performing a lookup for existing fingerprints; c. initiating a communication with a contact; d. determining whether speech has been detected; and e. computing a new fingerprint for the contact if an existing fingerprint is not found.
 48. The method of claim 47 further comprising the steps of: a. routing the communication to a human if one of: i. an existing fingerprint is not matched; and ii. existing fingerprints indicate a human is necessary for the contact, b. determining whether an end point of the communication has been associated with a means for answering a communication; c. creating a record associating the contact with a fingerprint; and d. saving said record.
 49. The method of claim 47 further comprising the steps of: a. performing at least one of the following actions if it is determined that a fingerprint match exists: disconnecting the communication, scheduling another communication at a new time, leaving a message with a means for answering a communication, routing the communication to a human, and determine an alternate contact and repeat process.
 50. The method of claim 47, wherein step (c) further comprises the step of supplying existing fingerprints associated with a contact to a media server.
 51. The method of claim 47, wherein a fingerprint comprises a unique identifier.
 52. The method of claim 47, wherein a fingerprint match in step (e) indicates an audio recording.
 53. The method of claim 47, wherein no fingerprint match in step (e) indicates a live person.
 54. The method of claim 47, wherein no fingerprint match in step (e) indicates a changed audio recording.
 55. The method of claim 47, further comprising the steps of: a. performing a record lookup within a database; and b. supplementing a record for a contact with an end result of a call detection.
 56. The method of claim 55, wherein the end result comprises one or more of: detected, changed, and missed.
 57. The endpoint of claim 47, wherein said endpoint comprises a human.
 58. The method of claim 47, further comprising the steps of: a. disconnecting said communication; b. re-initiating an other communication with said contact; c. determining whether said new fingerprint matches the re-initiated communication; and d. making an association with the means for answering, wherein i. if said new fingerprint matches, then classifying said means as an audio recording, and ii. if said new fingerprint does not match, then classifying said means as a human.
 59. A method for routing communications in a communication system, comprising the steps of: a. initiating a communication with a contact; and b. determining whether said contact, is associated with a known target wherein: i. if said contact is not associated with a known target, routing said communication to a human, and performing an action, and ii. if said contact is associated with a known target, performing an other action.
 60. The method of claim 59, wherein said action comprises the steps of: a. classifying said contact as a specific target; b. creating a fingerprint associated with said target; and c. storing said fingerprint to identify said target in future communications.
 61. The method of claim 59, wherein said other action comprises the steps of: a. performing at least one of the following: disconnecting the communication, scheduling another communication at a new time, leaving a message with a means for answering a communication, routing the communication to a human, and determine an alternate contact and repeat process.
 62. The method of claim 61, wherein said classifying as a specific target comprises a human denoting the target as an audio recording.
 63. The method of claim 59, wherein said initiating a communication comprises placing calls by means of an automated dialing system in a contact center. 